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FeSATLock: An Energy Efficient and SAT Attack Resilient Logic Locking Design With FeFET LUT Architecture for Enhanced Hardware Security
Full text linkBoolean satisfiability (SAT) attacks have been proven to be highly effective against logic locking techniques that secure intellectual property (IP). Prior research has improved the output corruptibility and SAT attack resiliency of logic locking, but often results in large overheads, higher design effort, increased delay and area/energy consumption. This work presents FeSATLock- a novel ferroelectric FET (FeFET) lookup table (LUT) based energy efficient and secure logic locking technique exploring the FeFET tunable device characteristics leading to both steep-slope characteristics for energy efficient circuit design and hysteresis behavior for non-volatile (NV) memory design. A FeFET LUT based key gate architecture has been proposed for key management and obfuscating the original circuit. A complete logic locking framework is demonstrated utilizing the proposed FeFET LUT based key gates, and performance has been benchmarked with baseline 40nm Complementary Metal Oxide Semiconductor Static Random Access Memory (CMOS SRAM) LUT based design at VDD=0.5 V. Due to the steep slope characteristics, at an optimal ferroelectric layer thickness ( tfe ), FeFET LUT key gate design achieves ∼2.68× lower energy consumption, and ∼6.01× higher speed with ~23% reduction in transistor count, compared to the baseline CMOS SRAM based key gate designs. The proposed FeFET LUT based locked circuit with key gates demonstrate ∼2.21× reduction in energy consumption and ~4.75x improvement in circuit speed in comparison to baseline CMOS SRAM based locked designs. The proposed logic locking design methodology is further evaluated against SAT attack, robustness is compared with the existing XOR, MUX based techniques and demonstrate higher SAT attack resiliency
Vitamin D Supplementation Enhances Cognitive Outcomes in Physically Active Vitamin D Deficient University Students in the United Arab Emirates: A 10-Week Intervention Study
Full text linkBackground/Objectives: Vitamin D deficiency is a global epidemic. In certain populations, such as the United Arab Emirates (UAE), low nutritional intake of vitamin D, inadequate exposure to sunlight, and cultural dress codes can lead to deficiencies in blood vitamin D levels, predisposing them to musculoskeletal disorders, diabetes, and cardiovascular diseases. There are also notable associations between vitamin D deficiency, physical inactivity, and lower cognitive performance. The aim of this study was to determine how vitamin D status may affect physical inactivity and cognitive performance in a young UAE population. Methods: Primary data were obtained on vitamin D status, cardiorespiratory fitness, body composition, and blood profiles of students in the UAE. Following initial assessment, a cohort of vitamin D-deficient/insufficient individuals participated in a 10-week physical activity intervention (Group A), whilst another cohort was supplemented with 5000 IU vitamin D3 daily and an exercise intervention (Group B). Both groups underwent physiological and biochemical profiling, and the effects of vitamin D supplementation on cognitive function were assessed. Statistical analysis included paired samples t-tests between pre- and post-intervention values and the Wilcoxon signed rank test for within-group comparisons and the Mann–Whitney U test for between-group comparisons. Results: The findings suggest that physical exercise alone improves overall cardiorespiratory fitness, as shown by an increased VO2 max (p < 0.05), while vitamin D supplementation combined with physical exercise did not significantly improve fitness over a 10-week period (p > 0.05). However, vitamin D combined with physical exercise significantly improved cognitive performance in Group B only, specifically in working memory, verbal memory, and cognitive flexibility (p < 0.05). Conclusions: This study highlights the need for targeted interventions such as physical exercise and vitamin D supplementation to be conducted at an early stage in order to improve physical and cognitive function and reduce the risk of disease
Adaptive Federated Learning Framework for Privacy-Preserving Consumer-Centric IoMT: A Novel Secure Data Collaboration Model
Full text linkWith the increasing adoption of Internet of Medical Things (IoMT) devices, modern healthcare systems face persistent challenges related to data privacy, device heterogeneity, communication overhead, and scalability especially in consumer electronics environments. This paper hypothesizes that a hierarchical and privacy-preserving federated learning architecture can address these challenges and enable efficient, secure, and personalized healthcare insights across distributed IoMT networks. To validate this, we present the Adaptive Federated Learning Framework (AFLF), a novel framework tailored for resource-constrained consumer devices such as wearable ECG monitors, glucose trackers, and portable neuro-sensors. AFLF incorporates four core components: (1) a hierarchical edge–fog–cloud model for scalable and latency-aware training, (2) a Secure Data Collaboration Protocol (SDCP) that combines blockchain-based audit trails with differential privacy to ensure integrity and confidentiality, (3) the Adaptive Personalized Federated Learning Algorithm (APFLA) that dynamically tunes learning rates and model weights for personalized inference, and (4) a ternary threshold-based gradient compression technique that reduces communication overhead by 45.3%. The framework is deployed on Raspberry Pi 4 (edge) and NVIDIA Jetson AGX Xavier (fog) platforms, using PyTorch Mobile for implementation. Experimental validation using four real-world datasets, CardioFit (ECG), GlucoWatch (glucose), PulseOx (PPG), and NeuroMotion (EEG), demonstrates that AFLF improves model accuracy by up to 12% reduces training time by 38% and preserves user privacy with a differential privacy budget of ε = 2.1. These results confirm that AFLF offers a robust and scalable privacy-preserving federated learning solution for next-generation consumer-centric smart healthcare applications
Safety fears and relocation stressors related to flawed buildings: Ireland’s defective concrete crisis
Full text linkThe use of defective concrete in the construction of buildings in Ireland has led to widespread property deterioration, displacement, financial loss, and psychological distress for thousands of families. No research to date has examined mental health outcomes or associated risk factors among affected individuals. This study aimed to generate estimates of probable major depressive disorder (MDD), probable generalized anxiety disorder (GAD), probable posttraumatic stress disorder (PTSD), probable complex PTSD (CPTSD), and suicidal ideation in a sample of this population and to identify crisis-related stressors associated with outcomes, while adjusting for trauma history, sociodemographic characteristics, and social support. A convenience sample of 393 adults completed a self-report survey between March and September 2024. Estimates were 30.4% for MDD, 26.2% for GAD, 4.9% for PTSD, and 15.5% for CPTSD. Suicidal ideation, experienced after a property was suspected to have defective concrete, was present in 35.5% of the sample. Safety fears were associated with CPTSD, MDD, and suicidal ideation, odds ratios (ORs) = 2.09–4.39, whereas GAD was associated with relocating, OR = 2.25. These findings highlight the substantial psychological impact of the crisis and identify specific stressors associated with increased risk for adverse outcomes
The Team Behind the Team: Factors Underpinning Group Cohesion Within High-Performance Sport Coaching and Athlete Support Teams
Full text linkBackground : High-performance sport organizations have become increasingly complex social environments, comprising of a diverse range of performance staff roles. Despite the prominence of high-performance sport coaching and athlete support teams (CaASTs), research concerning their processes is limited and has only begun to materialize. Group cohesion, a dynamic process reflecting how groups remain united to satisfy needs and pursue shared goals, is one aspect of group dynamics that may influence their performance. Aims : The aim of the present study was to examine the factors influencing group cohesion within the context of high-performance sport CaASTs. Methods : Qualitative data was collected through realistic theory-led interviews with coaches and practitioners operating across five high-performance sport programs. A total of 24 participants ( Mage = 45.43 years; Mservice = 4.13 years) discussed perceptions and experiences of factors influencing cohesion within their CaAST environment. Interviews lasted 47–96 min ( Mlength = 70 min). Reflexive thematic analysis was applied to identify themes and subthemes. Findings : Findings highlight four high-order themes and 26 low-order themes influencing group cohesion within the context of high-performance sport CaASTs. These findings aim to contribute to the growing literature on group dynamics within high-performance sport CaASTs, and to support applied practice within this context
Optimizing Heart Attack Detection with Brown-Bear Optimization Algorithm and CNN in Healthcare 4.0
Full text linkHeart disease is a non-communicable decease that lead to death if not treated. In the world most of the people died due to heart disease because they are not treated on time or their disease is not detected at early stage. Due to this efficient heart disease prediction techniques are important for the development of Healthcare 4.0. However, most of the current heart disease detection algorithms are either complex or not optimizes for efficient hyper-parameters. In this context, we proposed a CNN based lightweight heart disease detection framework (trained in 5 epoch). We also used random forest algorithm to identify the most important feature and Brown-Bear Algorithm for optimization of the hyper-parameter of CNN. We also compared the proposed model with current literature and present the efficiency of our proposed framework
Characterisation of a Biodegradable Electrode Substrate Based on Psyllium Husk–Carbon Nanoparticle Composites
Full text linkUnrefined psyllium husk derived from Plantago ovata constitutes a complex mixture of water-soluble and insoluble polymeric chains that form an interpenetrating network capable of entrapping carbon nanoparticles. While the resulting composite was found to swell in aqueous electrolyte, it exhibited hydrogel-like properties where the electrochemical activity was retained and found to be stable upon repetitive voltammetric cycling. Planar film systems were characterized by electron microscopy, Raman spectroscopy, tensile testing, gravimetric analysis, contact angle and cyclic voltammetry. A key advantage of the composite lies in its ability to be cast in 3D geometric forms such as pyramidal microneedle arrays (700 μm high × 200 μm base × 500 μm pitch) that could serve as viable electrode sensors. In contrast to conventional composite electrode materials that rely on non-aqueous solvents, the psyllium mixture is processed entirely from an aqueous solution. This, along with its plant-based origins and simple processing requirements, provides a versatile matrix for the design of biodegradable electrode structures that can be manufactured from more sustainable sources
Evaluation of vertebrobasilar arterial blood flow during HeartMate3 support via computational fluid dynamics analyses
Full text linkContinuous Flow Left Ventricular Assist Devices (CF-LVADs) are used to support the failing left ventricle in patients with end-stage heart failure. CF-LVADs unload the left ventricle continuously and generate non-physiological blood flow in the cardiovascular system, which may cause major complications, including neurological events such as haemorrhagic strokes. Therefore, quantifying the blood velocities and analysing altered blood flow in the cerebral circulation during CF-LVAD support will help to understand the effects of mechanical circulatory support on cerebral blood flow. The aim of this study is to evaluate blood flow in the vertebrobasilar arteries in a healthy condition and heart failure with reduced ejection fraction and during HeartMate 3 CF-LVAD support. Blood velocities and wall shear stresses in the vertebrobasilar arteries were evaluated using Computational Fluid Dynamics analyses for a healthy condition, heart failure with reduced ejection fraction and during HeartMate 3 support. Simulation results showed that time-averaged wall shear stress and relative residence time decrease in the vertebrobasilar arteries in heart failure. HeartMate 3 support provides comparable cerebral arterial average blood flow rates, pressures, time-averaged wall shear stresses and relative residence times to healthy conditions, although wall shear stresses and blood velocities are altered
Describing the Elements of an Effective Dementia Palliative Care Service
Full text linkBackgroundAs dementia is a life-limiting illness, it is now widely accepted that people with dementia benefit from palliative care. The core components of palliative care for people with dementia have been suggested, however little is known about what an effective dementia palliative care service looks like in practice. While some services exist, a lack of description and scant detail on how and why they work makes it difficult for others to learn from existing successful models and impedes replication. Accordingly, we set out to describe an effective dementia palliative care service using programme theory, and to visually represent it in a logic model.MethodsThis was mixed-methods study. An exemplary dementia palliative care service, which cares for people with advanced dementia in their own home in the last year of life, had been identified from a previous survey. The development of the programme logic model was informed by interviews with staff (n = 6), staff surveys (n = 1), service user surveys (n = 10) and the analysis of secondary data sources including routinely collected service data.ResultsThe logic model and summary results explain in detail how this dementia palliative care service undertook activities relating to person-centred care, carer support, end-of-life care, accessible care, timely care, and integrated care. It maps each activity to specific outputs and outcomes, showing that dementia palliative care, when provided appropriately, can greatly improve the quality of care received by people living and dying with advanced dementia, and their families, in the community.ConclusionsThe logic model presented may support those developing dementia palliative care services, or guide others running existing services in how to systematically present their service activities to others, and demonstrates how clinicians, policy-makers, and others involved in service planning can utilise logic models to design new services and improve existing services
Predictive Analytics of Air Quality for IoT- Enabled Industrial Environments
Full text linkAir quality is critical to the health, especially in industrial manufacturing environments. Pollutants such as fine particulate matter and toxic gases like CO2, NOx and VOCs are creating serious health risks. The limited ventilation at indoor manufacturing facilities makes them more vulnerable to poor air quality, causing serious health issues such as asthma and long-standing lung damage. Although existing air quality monitoring systems provide sensing capability for airborne particles or gases, they lack smart predictive capabilities to mitigate future risks in complex industrial and manufacturing environments. In this paper, we propose an air quality prediction solution that leverages real-time, streaming, timeseries data collected from IoT nodes deployed at different industrial locations. The system can monitor multiple pollutants, including PM2.5, PM10, CO2, NOx, and VOCs, using variations of Long Short-Term Memory (LSTM) networks to forecast contaminated air with high accuracy. Our approach involves thorough data preprocessing and analysis activities to effectively model each contaminant. The results show significant promise for forecasting and classifying air quality and offer industries a valuable tool to proactively manage indoor environmental conditions and protect human health